4 research outputs found
FaceForensics++: Learning to Detect Manipulated Facial Images
The rapid progress in synthetic image generation and manipulation has now
come to a point where it raises significant concerns for the implications
towards society. At best, this leads to a loss of trust in digital content, but
could potentially cause further harm by spreading false information or fake
news. This paper examines the realism of state-of-the-art image manipulations,
and how difficult it is to detect them, either automatically or by humans. To
standardize the evaluation of detection methods, we propose an automated
benchmark for facial manipulation detection. In particular, the benchmark is
based on DeepFakes, Face2Face, FaceSwap and NeuralTextures as prominent
representatives for facial manipulations at random compression level and size.
The benchmark is publicly available and contains a hidden test set as well as a
database of over 1.8 million manipulated images. This dataset is over an order
of magnitude larger than comparable, publicly available, forgery datasets.
Based on this data, we performed a thorough analysis of data-driven forgery
detectors. We show that the use of additional domainspecific knowledge improves
forgery detection to unprecedented accuracy, even in the presence of strong
compression, and clearly outperforms human observers.Comment: Video: https://youtu.be/x2g48Q2I2Z
Datasets, Clues and State-of-the-Arts for Multimedia Forensics: An Extensive Review
With the large chunks of social media data being created daily and the
parallel rise of realistic multimedia tampering methods, detecting and
localising tampering in images and videos has become essential. This survey
focusses on approaches for tampering detection in multimedia data using deep
learning models. Specifically, it presents a detailed analysis of benchmark
datasets for malicious manipulation detection that are publicly available. It
also offers a comprehensive list of tampering clues and commonly used deep
learning architectures. Next, it discusses the current state-of-the-art
tampering detection methods, categorizing them into meaningful types such as
deepfake detection methods, splice tampering detection methods, copy-move
tampering detection methods, etc. and discussing their strengths and
weaknesses. Top results achieved on benchmark datasets, comparison of deep
learning approaches against traditional methods and critical insights from the
recent tampering detection methods are also discussed. Lastly, the research
gaps, future direction and conclusion are discussed to provide an in-depth
understanding of the tampering detection research arena